Background: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist’s diagnoses. Objective: The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image–based discrimination between melanoma and nevus. Methods: Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image. Results: While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists’ average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step. Conclusions: The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image–based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions.

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中文翻译：

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人工智能及其对皮肤镜黑素瘤图像分类中皮肤科医生准确性的影响：基于Web的调查研究。

背景：黑色素瘤的早期发现可以挽救生命，但这仍然是一个挑战。最近的诊断研究揭示了人工智能（AI）在对黑色素瘤和痣的皮肤镜图像进行分类方面的优势，认为这些算法应有助于皮肤科医生的诊断。目的：本研究的目的是研究在基于二分图像的黑色素瘤和痣之间的鉴别中，AI支持是否可以提高皮肤科医生的准确性和整体诊断性能。方法：向十二名获得董事会认证的皮肤科医生介绍了100份皮肤镜下黑色素瘤和痣的独特影像（总共1200幅独特影像），他们仅凭个人经验就对影像进行了分类（第一部分），并得到了训练的卷积神经网络（CNN，第二部分）。另外，要求皮肤科医生对他们对每张图像的最终决定进行评估。结果：仅凭个人经验，皮肤科医生的平均特异性在AI支持下几乎保持不变（70.6％vs 72.4％； P = .54），但平均敏感性和平均准确性却显着提高（59.4％vs 74.6％; P = .003和65.0％对73.6％； P = .002，分别为AI支持。在皮肤科医生正确且AI不正确的10％（10/94; 95％CI 8.4％-11.8％）的情况下，平均而言，皮肤科医生对错误答案的回答为39％（4/10; 95％CI 23.2） ％-55.6％）的情况。当皮肤科医生不正确且AI正确时（25/94，27％; 95％CI 24.0％-30.1％），皮肤科医生将答案更改为正确答案为46％（11/25; 95％CI 33.1％-58.4％） ）的情况。另外，当CNN确认他们的决定时，皮肤科医生对他们决定的平均信心就会增加；而当CNN拒绝时，即使皮肤科医生正确，他们的平均信心也会下降。报告值基于所有参与者的平均值。每当显示绝对值时，分母和分子都是近似值，因为由于质量控制步骤，每位皮肤科医生最终对不同数量的图像进行评级。结论：我们的研究结果表明，AI支持可以改善皮肤科医生在黑素瘤和痣之间基于二分图像的区分中的整体准确性。这支持了基于AI的工具来帮助临床医生进行皮肤病变分类的论点，并为在现实环境中研究此类分类器提供了理论依据，

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